A Fast and Lossless Image Compression (FLIC) algorithm based on the median edge predictor and Golomb coder of JPEG-LS is presented. FLIC eliminates the gradient-based context model from the JPEG-LS standard, the most expensive parts with respect to computational complexity and memory space requirements. To avoid a large context memory, Golomb parameter is selected based on the coding states and the prediction residuals of up to two immediate neighbors, one in each dimension. The FLIC algorithm has low memory footprint and dissolves the data dependencies in JPEG-LS to facilitate parallelization. Experimental results show that the FLIC algorithm achieves a throughput speedup factor of 3.7 over JPEG-LS with less than 4% compression performance penalty. Lossless compression performance results further show that FLIC outperforms other state-of-the-art standards including JPEG 2000 and JPEG XR.
%0 Conference Paper
%1 wang2012lossless
%A Wang, Z.
%A Klaiber, M.
%A Gera, Y.
%A Simon, S.
%A Richter, T.
%B Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European
%D 2012
%K Golomb XR;JPEG-LS;computational adaptation;FLIC algorithm;Golomb coder;Golomb coding;2D coding;JPEG-LS;Lossless coding;Memory coding;low coding;parallelization complexity complexity;data complexity;fast compression;adaptive compression;gradient-based compression;image computational context edge image lossless management;Throughput;Transform model;median modeling;Encoding;Image parameter parameter;JPEG predictor;memory requirements;Context;Context space
%P 1920-1924
%T Fast lossless image compression with 2D Golomb parameter adaptation based on JPEG-LS
%U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6334076
%X A Fast and Lossless Image Compression (FLIC) algorithm based on the median edge predictor and Golomb coder of JPEG-LS is presented. FLIC eliminates the gradient-based context model from the JPEG-LS standard, the most expensive parts with respect to computational complexity and memory space requirements. To avoid a large context memory, Golomb parameter is selected based on the coding states and the prediction residuals of up to two immediate neighbors, one in each dimension. The FLIC algorithm has low memory footprint and dissolves the data dependencies in JPEG-LS to facilitate parallelization. Experimental results show that the FLIC algorithm achieves a throughput speedup factor of 3.7 over JPEG-LS with less than 4% compression performance penalty. Lossless compression performance results further show that FLIC outperforms other state-of-the-art standards including JPEG 2000 and JPEG XR.
@inproceedings{wang2012lossless,
abstract = {A Fast and Lossless Image Compression (FLIC) algorithm based on the median edge predictor and Golomb coder of JPEG-LS is presented. FLIC eliminates the gradient-based context model from the JPEG-LS standard, the most expensive parts with respect to computational complexity and memory space requirements. To avoid a large context memory, Golomb parameter is selected based on the coding states and the prediction residuals of up to two immediate neighbors, one in each dimension. The FLIC algorithm has low memory footprint and dissolves the data dependencies in JPEG-LS to facilitate parallelization. Experimental results show that the FLIC algorithm achieves a throughput speedup factor of 3.7 over JPEG-LS with less than 4% compression performance penalty. Lossless compression performance results further show that FLIC outperforms other state-of-the-art standards including JPEG 2000 and JPEG XR.},
added-at = {2016-03-10T09:18:49.000+0100},
author = {Wang, Z. and Klaiber, M. and Gera, Y. and Simon, S. and Richter, T.},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2e8927761727a9bf3e5dd6858aedd7b56/thomasrichter},
booktitle = {Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European},
interhash = {c059b3eef64a56bcf5eefb9472dcec8e},
intrahash = {e8927761727a9bf3e5dd6858aedd7b56},
issn = {2219-5491},
keywords = {Golomb XR;JPEG-LS;computational adaptation;FLIC algorithm;Golomb coder;Golomb coding;2D coding;JPEG-LS;Lossless coding;Memory coding;low coding;parallelization complexity complexity;data complexity;fast compression;adaptive compression;gradient-based compression;image computational context edge image lossless management;Throughput;Transform model;median modeling;Encoding;Image parameter parameter;JPEG predictor;memory requirements;Context;Context space},
month = aug,
pages = {1920-1924},
timestamp = {2016-03-10T08:20:00.000+0100},
title = {{F}ast lossless image compression with 2{D} {G}olomb parameter adaptation based on {JPEG}-{LS}},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6334076},
year = 2012
}
